A Study on the Impact of Generative Artificial Intelligence on Creative Convergence Problem Solving
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The objective of this study is to examine the factors that shape problem-solving processes through the lens of generative artificial intelligence (AI) and to develop an educational model that integrates generative AI into the problem-solving process. To facilitate analysis, we divided the problem-solving process into three stages: understanding the problem, designing the solution, and selecting the best solution. Subsequently, we aligned each stage with the nine core elements of computational thinking to develop the educational framework. Following the development of a generative AI-based software engineering course based on the design, a correlation analysis was conducted on the necessity of generative AI in the problem-solving process. This analysis was performed after providing 218 learners of humanities and social sciences with a 16-week basic software engineering education. The results of the hierarchical regression analysis demonstrated that generative AI exerted an influence when data were analyzed during the problem understanding stage (t-value = 8.85, R Square = 0.712). In the design stage of the problem-solving process, generative AI had a significant impact on the algorithms and procedures step (t-value = 13.810, R Square = 0.713). In the selection of the optimal solution, generative AI was found to exert a considerable influence in the automation stage (t-value = 7.764, R Square = 0.691). In light of these findings, it was concluded that the integration of generative AI into the stages of data analysis, algorithms and procedures, and automation in the educational curriculum could enhance learners' problem-solving abilities. It is our contention that this research will facilitate future research on learning guidelines for generative AI.
Abstract
The objective of this study is to examine the factors that shape problem-solving processes through the lens of generative artificial intelligence (AI) and to develop an educational model that integrates generative AI into the problem-solving process. To facilitate analysis, we divided the problem-solving process into three stages: understanding the problem, designing the solution, and selecting the best solution. Subsequently, we aligned each stage with the nine core elements of computational thinking to develop the educational framework. Following the development of a generative AI-based software engineering course based on the design, a correlation analysis was conducted on the necessity of generative AI in the problem-solving process. This analysis was performed after providing 218 learners of humanities and social sciences with a 16-week basic software engineering education. The results of the hierarchical regression analysis demonstrated that generative AI exerted an influence when data were analyzed during the problem understanding stage (t-value = 8.85, R Square = 0.712). In the design stage of the problem-solving process, generative AI had a significant impact on the algorithms and procedures step (t-value = 13.810, R Square = 0.713). In the selection of the optimal solution, generative AI was found to exert a considerable influence in the automation stage (t-value = 7.764, R Square = 0.691). In light of these findings, it was concluded that the integration of generative AI into the stages of data analysis, algorithms and procedures, and automation in the educational curriculum could enhance learners' problem-solving abilities. It is our contention that this research will facilitate future research on learning guidelines for generative AI.
참고문헌 (28)
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